用微控制器实时检测压力，精度达93.7%
Real-Time Stress Detection via Photoplethysmogram Signals: Implementation of a Combined Continuous Wavelet Transform and Convolutional Neural Network on Resource-Constrained Microcontrollers
- 结合小波变换与卷积神经网络分析腕部血容脉搏信号
- 模型压缩至1.6MB，5轮训练后准确率达93.7%
- 适合资源受限的可穿戴设备，支持实时压力监测
本文提出一种基于卷积神经网络（CNN）的鲁棒压力检测系统，用于分析腕部光电容积脉搏波（PPG）信号。利用WESAD数据集，通过连续小波变换（CWT）提取特征，显著提升压力检测性能。模型在资源受限的微控制器上实现，经剪枝与量化后体积缩小至1.6兆字节，克服了2兆字节闪存和512千字节内存的限制。仅经过5轮训练，准确率即达93.7%，优于传统信号处理方法，为可穿戴设备上的实时压力监测提供可行方案。
原文摘要 · Abstract (English)
This paper introduces a robust stress detection system utilizing a Convolutional Neural Network (CNN) designed for the analysis of Photoplethysmogram (PPG) signals. Employing the WESAD dataset, we applied Continuous Wavelet Transform (CWT) to extract informative features from wrist PPG signals, demonstrating enhanced stress detection and learning compared to conventional techniques. Notably, the CNN achieved an impressive accuracy of 93.7% after five epochs, post-implementation on a resource-constrained microcontroller. The optimization process, including pruning and Post-Train Quantization, was crucial to reduce the model size to 1.6 megabytes, overcoming the microcontroller's limited resources of 2 megabytes of Flash memory and 512 kilobytes of RAM. This optimized model not only addresses resource constraints but also outperforms traditional signal processing methods, positioning it as a promising solution for real-time stress monitoring on wearable devices.
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